@inproceedings{chen-etal-2025-sysupporter,
    title = "{SYSU}pporter Team at {BEA} 2025 Shared Task: Class Compensation and Assignment Optimization for {LLM}-generated Tutor Identification",
    author = "Chen, Longfeng  and
      Huang, Zeyu  and
      Xiao, Zheng  and
      Zeng, Yawen  and
      Xu, Jin",
    editor = {Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bea-1.83/",
    pages = "1078--1083",
    ISBN = "979-8-89176-270-1",
    abstract = "In this paper, we propose a novel framework for the tutor identification track of the BEA 2025 shared task (Track 5). Our framework integrates data-algorithm co-design, dynamic class compensation, and structured prediction optimization. Specifically, our approach employs noise augmentation, a fine-tuned DeBERTa-v3-small model with inverse-frequency weighted loss, and Hungarian algorithm-based label assignment to address key challenges, such as severe class imbalance and variable-length dialogue complexity. Our method achieved 0.969 Macro-F1 score on the official test set, securing second place in this competition. Ablation studies revealed significant improvements: a 9.4{\%} gain in robustness from data augmentation, a 5.3{\%} boost in minority-class recall thanks to the weighted loss, and a 2.1{\%} increase in Macro-F1 score through Hungarian optimization. This work advances the field of educational AI by providing a solution for tutor identification, with implications for quality control in LLM-assisted learning environments."
}